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REVIEW 4 major objections 5 minor 45 references

An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that the minimum-cost intervention that resolves an anomaly can be found by recovering hidden noise variables from clustered abnormal data and optimizing a necessity-constrained cost objective.

desk verdict The paper's integration is sensible and its experiments are strong, but the central optimization problem is undefined as written—the PN constraint is never actually computed—so the main claim does not hold. read the letter →

arxiv 2505.08343 v1 pith:MTEEC453 submitted 2025-05-13 cs.AI

Ruichu Cai , Xi Chen , Jie Qiao , Zijian Li , Yuequn Liu , Wei Chen , Keli Zhang , Jiale Zheng This is my paper · ORCID
classification cs.AI
keywords causaldecision-makingcounterfactualreasoningidentifiabilityminimum-costinterventionrootcauseanalysisprobabilityofnecessitysurrogatemodelvariationalautoencoder
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Decision-making under anomalies is usually split between trial-and-error reinforcement learning, which ignores cost, and root cause analysis, which ignores whether the fix is feasible or affordable. This paper tries to establish a third route: cast the decision as a counterfactual intervention and find the cheapest intervention vector that makes the anomalous target return to normal with high probability. To do that, it must make counterfactual reasoning identifiable from mixed abnormal data, and it does so by clustering abnormal patterns and using the cluster labels to recover the exogenous noise variables of a structural causal model. Given the approximated model, the framework optimizes intervention cost subject to a probability-of-necessity constraint. If the claims hold, operators could get a concrete lowest-cost fix recommendation rather than a ranked list of root causes.

What carries the argument

The load-bearing object is the surrogate causal model: a variational autoencoder whose decoder respects the causal graph, whose encoder inverts the structural equations to recover the exogenous noise $Z$ from each variable, its parents, and the cluster label $u$, and whose training uses an SCM-informed evidence lower bound. The cluster labels come from a Gaussian mixture model over abnormal samples; Lemma 1 guarantees the mixture is identifiable when there are at least $2K$ weakly separable variables, and Lemma 2 guarantees the recovered latent noise is identifiable under an exponential-family conditional prior with enough distinct cluster points. The optimizer is Sequential Least Squares Programming applied to the Lagrangian of the cost function with the probability-of-necessity (PN) constraint, where PN is the counterfactual probability that intervening to $x^*$ flips the anomalous target to normal.

What would settle it

Generate synthetic SCMs with $K$ anomaly patterns but fewer than $2K$ weakly separable variables, or merge two distinct noise regimes into one cluster, then fit MiCCD and test whether two observationally equivalent SCMs produce different recovered noise variables and different optimal intervention vectors; divergence would show the identifiability guarantee does not hold in that regime.

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Extended reading notes

Core claim

The central claim is that the minimum-cost intervention vector $x^*$ satisfying $P(Y_{do(X=x^*)} = 0 \mid X=x, Y=1) \ge \iota$ can be identified without online trials, by doing counterfactual reasoning on an estimated structural causal model. The supporting discovery is that the exogenous noise variables $Z$ are identifiable from large amounts of mixed anomaly data when abnormal-pattern cluster labels $u$ are used as supervision: the clusters separate distinct noise-shift regimes, and a causal-graph-structured variational autoencoder trained with those labels recovers the noise variables that drive the anomaly. With the noise recovered, abduction, intervention, and prediction produce counterfactual outcomes for candidate interventions, and SLSQP searches the continuous intervention space for the minimum-cost vector that meets the necessity constraint. On synthetic graphs and on three real datasets, the paper reports that this procedure achieves lower intervention cost and higher F1 and nDCG@k than RCA, LIME, LC, and a cost-aware baseline.

Load-bearing premise

The argument stands or falls on the assumption that the GMM cluster labels $u$ correspond one-to-one to the true abnormal noise regimes, with at least $2K$ weakly separable variables; if the clusters do not match the real noise shifts, the recovered noise variables are not identifiable and the counterfactual predictions that drive the optimizer become unreliable.

Editorial extensions

If this is right

  • Because the intervention search is guided by counterfactual predictions rather than trial-and-error, the framework can recommend fixes in settings where online experimentation is too expensive or unsafe.
  • The probability-of-necessity constraint acts as a validity filter, so the output is not just a cheap action but one with a quantified chance of actually resolving the anomaly.
  • The recovered noise variables give per-sample explanations of why the system is abnormal, linking the decision output back to root-cause-style diagnosis.
  • Experimental results, if taken at face value, suggest the framework keeps its advantage as graphs grow from 5 to 20 nodes and as sparsity and edge weights vary.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the same abduction-intervention-prediction loop could be applied to personalized treatment selection, where clusters of patient trajectories play the role of abnormal patterns and symptom remission replaces the target threshold.
  • Editorial inference: the paper's restriction to observed variables is a stated development boundary; a natural extension is to treat unobserved confounders as additional noise components and cluster on their signatures, which would extend the identifiability argument to latent-variable graphs.
  • Editorial inference: because the identifiability guarantee depends on the number of weakly separable variables, a stress test on real datasets with fewer distinct anomaly regimes could reveal a minimum data complexity below which the cost-optimal intervention is not reliable.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes MiCCD, a causal decision-making framework that aims to find a minimum-cost intervention vector x* satisfying a probability-of-necessity (PN) constraint in abnormal states. The framework has two components: a surrogate model that uses GMM cluster labels as auxiliary supervision to recover exogenous noise variables through a causal-structure-aware variational autoencoder, and an optimization stage that uses the surrogate to evaluate counterfactual outcomes and applies SLSQP to minimize intervention cost under the PN constraint. The claimed contributions are identifiability of the counterfactual reasoning and cost-optimal intervention selection; experiments on synthetic data and three real-world datasets compare MiCCD against RCA and cost-based baselines on F1, N-Cost, nDCG@k, and counterfactual r-MSE.

Significance. If the technical claims were valid, MiCCD would be a useful contribution to cost-aware causal decision-making: it couples identifiable noise recovery with continuous optimization over interventions and evaluates on realistic datasets. The paper sensibly builds on external identifiability results (Tahmasebi et al., Khemakhem et al.) rather than attempting a proof from scratch, and the experimental comparison spans multiple baselines and four datasets. However, the central optimization problem is not actually well-defined: the PN constraint in Eq. (2) is never given a computable estimator, and the identifiability argument in Section 4.1.1 contains an unjustified '2d = 2K' claim. Because these issues concern the core of the proposed method rather than presentation, the current manuscript does not support its headline claims.

major comments (4)
  1. [4.2.2, Eqs. (2) and (9)] The paper never defines a computable estimator for the PN constraint P(Y_do(X=x*)=0 | X=x, Y=1) in Eq. (2). The counterfactual estimation procedure in Section 4.2.2 produces a single deterministic outcome y* via abduction, intervention, and prediction; no Monte Carlo averaging or posterior integration over the variational noise q(z_j | x_PA_j, x_j, u) from Eq. (8) is specified, and no formula connects that posterior to the value of g(x*) in Eq. (9). Consequently the constrained optimization problem in Eq. (2) is not well-defined, and the SLSQP/KKT discussion has no constraint function to enforce; this invalidates the central claim that MiCCD identifies a minimum-cost intervention vector satisfying the PN threshold.
  2. [4.1.1, Lemma 1 and its Implication] The step 'at least 2d = 2K weakly separable variables satisfy the identifiable conditions' is not justified. In Lemma 1, K is the number of mixture components and L_w is a property of the mixture; the paper does not define L_w, does not define K in relation to the number of variables d, and gives no argument that each of the d variables contributes exactly two weakly separable distributions. Since the GMM cluster count K is a free hyperparameter that need not equal d, the condition L_w >= 2K cannot be concluded from the stated abnormality assumptions, and the identifiability of the clustering (and hence the auxiliary labels u) is not established.
  3. [4.1.2, Lemma 2] The paper invokes Lemma 2 as the theoretical foundation for noise recovery but never verifies its conditions A1–A3 for the proposed causal graph, decoder class, or cluster-label variable u. In particular, condition A3 requires nk+1 distinct auxiliary values with an invertible matrix L, and the paper does not explain how the GMM cluster labels provide such points or how many clusters are needed. Moreover, even if the latent variables Z are identifiable up to the equivalence in Lemma 2, the paper does not prove that the counterfactual probability PN and the optimal intervention vector x* are invariant under that equivalence; this is a further unstated gap in the claim of identifiable counterfactual reasoning.
  4. [5.1, 5.2, and Table 1] The empirical evaluation does not specify the intervention cost function C, the PN threshold iota, or the SLSQP settings used to produce the reported N-Cost and F1 results, and the clustering quality is validated only on a synthetic 10-node chain (Figure 7), not on the real datasets. Because the optimization problem is undefined without these components (Major Comment 1), the experimental comparisons cannot be independently reproduced, and the claimed superiority in N-Cost cannot be attributed to the proposed constrained optimization rather than to unspecified choices in the cost function or solver.
minor comments (5)
  1. [Section 1] There are typos such as 'anormaly pattern' and 'produce'; please proofread the manuscript carefully.
  2. [Section 3 and Rule 1] The notation for the intervention set is inconsistent: Eq. (2) uses x* ∈ R^n while Rule 1 writes X* and X*_R; define the relationship between the vector and the set.
  3. [Section 5.2] The text claims an improvement of 'up to 44 percentage points' but the values in Table 1 imply different margins (e.g., 0.94-0.44=0.50 on Lemma-RCA); please recompute or clarify the stated margins.
  4. [Appendix D] 'Thr reconstruction results' and 'nncludes' in Section 5.1 should be corrected; Table D.4's column header formatting (Chain/Sparsity) should align with Table A.3.
  5. [Section 4.2.1] The GMM component is described as providing auxiliary labels, but the number of components K and the initialization/selection procedure are not reported; this is needed to assess whether the assumed one-to-one mapping to anomaly patterns is plausible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: identifiability results are imported from external work, the surrogate is a standard ELBO-trained VAE, and counterfactual r-MSE is evaluated on held-out test data.

full rationale

The paper's derivation chain does not reduce its conclusions to its inputs. The identifiability lemmas (Lemma 1 from Tahmasebi et al. 2018; Lemma 2 from Khemakhem et al. 2020) are external results, not self-citations, and the paper's own contribution is a surrogate VAE plus an SLSQP optimization. The surrogate objective in Eq. 8 is a standard SCM-informed ELBO; the encoder infers noise z from x and u, and the decoder reconstructs x, so no fitted parameter is renamed as a prediction. Held-out r-MSE (Table 2) is a genuine test of counterfactual reconstruction. The nearest circularity-adjacent element is the GMM-derived cluster label u: u is estimated from the same observed data and then used as an auxiliary supervision signal for noise recovery. This raises a question about whether Lemma 2's assumptions (an auxiliary variable u with an invertible lambda-difference matrix) are satisfied by an estimated clustering, but the paper does not use that identification to define the target it claims to predict; the target intervention vector is optimized against cost and PN constraints. Any gap here is an assumption/verification issue, not a self-definitional equivalence. The paper also notes in Sec. 5.1 that hidden-variable support 'remains under development,' and the PN constraint in Eq. 2 is never given an explicit Monte Carlo estimator in Sec. 4.2.2; both are correctness or completeness concerns, not circularity. Under the hard rule that a circularity finding requires an exhibited equation-level reduction or a fitted parameter called a prediction, no such step exists.

Assumptions & free parameters 4 free parameters · 5 assumptions · 1 invented entities

The central claim depends on several unverified assumptions: known acyclic graph, Gaussian noise-shift anomalies, injective mechanisms, and especially the correctness of GMM clusters as ground-truth abnormal patterns. The free parameters (cluster count, PN threshold, cost function, anomaly threshold) are not specified, which makes the method difficult to reproduce and weakens the identifiability argument.

free parameters (4)
  • Number of GMM clusters K = not specified
    Must match the number of abnormal noise regimes for Lemma 2 to apply; no value is reported or tuned in experiments.
  • PN threshold iota = not specified
    Constraint threshold in Eq. 2; the paper never states what value was used.
  • Intervention cost function C = not specified
    Defines the optimization objective; the paper says the same C is used for MiCCD and AUF-MICNS but never gives its form.
  • Anomaly threshold t on Y = not specified
    Binarizes the target for anomaly detection and for evaluating PN; no value is reported.
assumptions (5)
  • domain assumption The causal graph G is known, acyclic, and correctly represents the data generation process
    Invoked throughout Section 3 and 4; real experiments rely on fixed expert graphs and hidden variables are ignored.
  • domain assumption Anomalies are caused only by shifts in the Gaussian noise distributions of observed variables
    Problem formulation in Section 3 restricts the anomaly model and rules out structural changes or hidden confounders.
  • domain assumption The structural functions f_i are injective and the conditional noise is a factorial exponential family
    These are conditions A1-A2 required by Lemma 2 for iVAE identifiability; not verified on real data.
  • ad hoc to paper GMM cluster labels u correspond one-to-one to true abnormal noise regimes and the data satisfy the weakly separable condition of Lemma 1
    Section 4.1.1 asserts this via an unproven '2d = 2K' argument that appears to conflate variable count with mixture component count.
  • domain assumption There are no hidden confounders or hidden mediators
    The paper explicitly says hidden-variable handling is under development in Section 5.1, so the framework assumes all relevant variables are observed.
invented entities (1)
  • Anomaly pattern label u (cluster assignment)
    purpose: Serves as the auxiliary variable that makes the latent noise variables identifiable and supervises the VAE encoder
    This is a modeling construct estimated by GMM on the same training data; the paper provides no independent measurement or falsifiable prediction tied to u.

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Cite this review

Pith. "Pith review of An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning." pith.science (2026). https://pith.science/paper/MTEEC453

@misc{pith2026250508343,
  author       = {Pith},
  title        = {Pith review of: An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MTEEC453}},
  note         = {Machine review of arXiv:2505.08343}
}
read the original abstract

Decision making under abnormal conditions is a critical process that involves evaluating the current state and determining the optimal action to restore the system to a normal state at an acceptable cost. However, in such scenarios, existing decision-making frameworks highly rely on reinforcement learning or root cause analysis, resulting in them frequently neglecting the cost of the actions or failing to incorporate causal mechanisms adequately. By relaxing the existing causal decision framework to solve the necessary cause, we propose a minimum-cost causal decision (MiCCD) framework via counterfactual reasoning to address the above challenges. Emphasis is placed on making counterfactual reasoning processes identifiable in the presence of a large amount of mixed anomaly data, as well as finding the optimal intervention state in a continuous decision space. Specifically, it formulates a surrogate model based on causal graphs, using abnormal pattern clustering labels as supervisory signals. This enables the approximation of the structural causal model among the variables and lays a foundation for identifiable counterfactual reasoning. With the causal structure approximated, we then established an optimization model based on counterfactual estimation. The Sequential Least Squares Programming (SLSQP) algorithm is further employed to optimize intervention strategies while taking costs into account. Experimental evaluations on both synthetic and real-world datasets reveal that MiCCD outperforms conventional methods across multiple metrics, including F1-score, cost efficiency, and ranking quality(nDCG@k values), thus validating its efficacy and broad applicability.

Figures

Figures reproduced from arXiv: 2505.08343 by the authors.

Figure 1
Figure 1. A toy decision-making example. If a data center ( [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The MiCCD framework comprises two components: (a) a surrogate model that [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. An incomplete causal relationship diagram after removing hidden variables [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Causes of PM2.5 in Beijing before APEC were constructed using randomly generated causal graphs with n = 5, 10, 20 vari￾ables. The graph structures included both chain and random topologies, with sparsity levels set to 0.2 and 0.3. The weights of the causal edges were s…
Figure 5
Figure 5. Figure 5: Comparison of Results under Varying Experimental Settings. The per￾formance of different methods is evaluated under three varying settings: the number of nodes (left), the sparsity level of the causal graph (middle), and the strength of causal edge weights (right). In …
Figure 6
Figure 6. Figure 6: Comparison of nDCG@k Values under Varying Numbers of Nodes. The nDCG@k values are compared across different values of k and varying numbers of nodes. Across all configurations, MiCCD consistently yields higher nDCG@k scores relative to baseline approaches, indicating i…
Figure 7
Figure 7. Figure 7: Confusion matrix heat map: Clustering results when the dataset contains 10 [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]

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Works this paper leans on

45 extracted references · 35 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTIO...

  2. [2]

    Root cause identification for collective anomalies in time series given an acyclic summary causal graph with loops

    Charles K Assaad, Imad Ez-Zejjari, and Lei Zan. Root cause identification for collective anomalies in time series given an acyclic summary causal graph with loops. In International Conference on Artificial Intelligence and Statistics , pages 8395--8404. PMLR, 2023

    work page 2023

  3. [3]

    Sequential quadratic programming

    Paul T Boggs and Jon W Tolle. Sequential quadratic programming. Acta numerica , 4:1--51, 1995

    work page 1995

  4. [4]

    Causal structure-based root cause analysis of outliers

    Kailash Budhathoki, Lenon Minorics, Patrick Bl \"o baum, and Dominik Janzing. Causal structure-based root cause analysis of outliers. In International Conference on Machine Learning , pages 2357--2369. PMLR, 2022

    work page 2022

  5. [5]

    Explainable machine learning in industry 4.0: Evaluating feature importance in anomaly detection to enable root cause analysis

    Mattia Carletti, Chiara Masiero, Alessandro Beghi, and Gian Antonio Susto. Explainable machine learning in industry 4.0: Evaluating feature importance in anomaly detection to enable root cause analysis. In 2019 IEEE international conference on systems, man and cybernetics (SMC) , pages 21--26. IEEE, 2019

    work page 2019

  6. [6]

    Visualizing the feature importance for black box models

    Giuseppe Casalicchio, Christoph Molnar, and Bernd Bischl. Visualizing the feature importance for black box models. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10--14, 2018, Proceedings, Part I 18 , pages 655--670. Springer, 2019

    work page 2018

  7. [7]

    Maximum likelihood from incomplete data via the em algorithm

    Arthur P Dempster, Nan M Laird, and Donald B Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the royal statistical society: series B (methodological) , 39(1):1--22, 1977

    1977

  8. [8]

    Deeplog: Anomaly detection and diagnosis from system logs through deep learning

    Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar. Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security , pages 1285--1298, 2017

    work page 2017

Show all 45 references
  1. [9]

    Avoiding undesired future with minimal cost in non-stationary environments

    Wen-Bo Du, Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou. Avoiding undesired future with minimal cost in non-stationary environments. In The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024

    2024

  2. [10]

    A framework for human-computer interactive street network design based on a multi-stage deep learning approach

    Zhou Fang, Jiaxin Qi, Lubin Fan, Jianqiang Huang, Ying Jin, and Tianren Yang. A framework for human-computer interactive street network design based on a multi-stage deep learning approach. Computers, Environment and Urban Systems , 96:101853, 2022

    2022

  3. [11]

    Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

    Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International conference on machine learning , pages 1861--1870. PMLR, 2018

    2018

  4. [12]

    Deep reinforcement learning for optimizing finance portfolio management

    Yuh-Jong Hu and Shang-Jen Lin. Deep reinforcement learning for optimizing finance portfolio management. In 2019 amity international conference on artificial intelligence (AICAI) , pages 14--20. IEEE, 2019

    2019

  5. [13]

    Collaboration failure analysis in cyber-physical system-of-systems using context fuzzy clustering

    Sangwon Hyun, Eunkyoung Jee, and Doo-Hwan Bae. Collaboration failure analysis in cyber-physical system-of-systems using context fuzzy clustering. Empirical Software Engineering , 30(2):1--29, 2025

    2025

  6. [14]

    Anomaly attribution with likelihood compensation

    Tsuyoshi Id \'e , Amit Dhurandhar, Ji r \' Navr \'a til, Moninder Singh, and Naoki Abe. Anomaly attribution with likelihood compensation. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 4131--4138, 2021

    2021

  7. [15]

    Variational autoencoders and nonlinear ica: A unifying framework

    Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen. Variational autoencoders and nonlinear ica: A unifying framework. In International conference on artificial intelligence and statistics , pages 2207--2217. PMLR, 2020

    2020

  8. [16]

    Offline reinforcement learning with implicit q-learning

    Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit q-learning. In International Conference on Learning Representations , 2022

    2022

  9. [17]

    Conservative q-learning for offline reinforcement learning

    Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning. Advances in Neural Information Processing Systems , 33:1179--1191, 2020

    2020

  10. [18]

    Adrien Marie Legendre. Nouvelles m \'e thodes pour la d \'e termination des orbites des com \`e tes: avec un suppl \'e ment contenant divers perfectionnemens de ces m \'e thodes et leur application aux deux com \`e tes de 1805 . Courcier, 1806

  11. [19]

    Challenging common assumptions in the unsupervised learning of disentangled representations

    Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Sch \"o lkopf, and Olivier Bachem. Challenging common assumptions in the unsupervised learning of disentangled representations. In international conference on machine learning , pages 4114-...

    2019

  12. [20]

    Human-level control through deep reinforcement learning

    Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature , 518(7540):529--533, 2015

    2015

  13. [21]

    Root cause explanation of outliers under noisy mechanisms

    Phuoc Nguyen, Truyen Tran, Sunil Gupta, Thin Nguyen, and Svetha Venkatesh. Root cause explanation of outliers under noisy mechanisms. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 38, pages 20508--20515, 2024

    2024

  14. [22]

    Unsupervised root-cause analysis for integrated systems

    Renjian Pan, Zhaobo Zhang, Xin Li, Krishnendu Chakrabarty, and Xinli Gu. Unsupervised root-cause analysis for integrated systems. In 2020 IEEE International Test Conference (ITC) , pages 1--10. IEEE, 2020

    2020

  15. [23]

    Causality

    Judea Pearl. Causality . Cambridge university press, 2009

    2009

  16. [24]

    Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation

    David MW Powers. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061 , 2020

    2010 arXiv

  17. [25]

    Rehearsal learning for avoiding undesired future

    Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou. Rehearsal learning for avoiding undesired future. Advances in Neural Information Processing Systems , 36, 2024

    2024

  18. [26]

    Counterfactual root cause analysis via anomaly detection and causal graphs

    Josephine Rehak, Anouk Sommer, Maximilian Becker, Julius Pfrommer, and J \"u rgen Beyerer. Counterfactual root cause analysis via anomaly detection and causal graphs. In 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) , pages 1--7. IEEE, 2023

    2023

  19. [27]

    Anchors: High-precision model-agnostic explanations

    Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Anchors: High-precision model-agnostic explanations. In Proceedings of the AAAI conference on artificial intelligence , volume 32, 2018

    2018

  20. [28]

    An evaluation framework for machine learning and data science (ml/ds) based financial strategies: A case study driven decision model

    Mohammadsaleh Saadatmand, Tugrul Daim, Carlos Mena, Haydar Yalcin, Gulin Bolatan, and Manali Chatterjee. An evaluation framework for machine learning and data science (ml/ds) based financial strategies: A case study driven decision model. IEEE Transactions on Engineering Manag...

    2024

  21. [29]

    Automated machine learning: Ai-driven decision making in business analytics

    Marc Schmitt. Automated machine learning: Ai-driven decision making in business analytics. Intelligent Systems with Applications , 18:200188, 2023

    2023

  22. [30]

    Trust region policy optimization

    John Schulman, Sergey Levine, Pieter Abbeel, Michael I Jordan, and Philipp Moritz. Trust region policy optimization. In Proceedings of the 32nd International Conference on Machine Learning , pages 1889--1897. PMLR, 2015

    2015

  23. [31]

    Proximal policy optimization algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 , 2017

    2017 arXiv

  24. [32]

    An expert system for highway construction: Multi-objective optimization using enhanced particle swarm for optimal equipment management

    Ali Shehadeh, Odey Alshboul, Khaled F Al-Shboul, and Omer Tatari. An expert system for highway construction: Multi-objective optimization using enhanced particle swarm for optimal equipment management. Expert Systems with Applications , 249:123621, 2024

    2024

  25. [33]

    Sample-specific root causal inference with latent variables

    Eric Strobl and Thomas A Lasko. Sample-specific root causal inference with latent variables. In Conference on Causal Learning and Reasoning , pages 895--915. PMLR, 2023

    2023

  26. [34]

    An efficient explanation of individual classifications using game theory

    Erik Strumbelj and Igor Kononenko. An efficient explanation of individual classifications using game theory. The Journal of Machine Learning Research , 11:1--18, 2010

    2010

  27. [35]

    On the identifiability of finite mixtures of finite product measures

    Behrooz Tahmasebi, Seyed Abolfazl Motahari, and Mohammad Ali Maddah-Ali. On the identifiability of finite mixtures of finite product measures. arXiv preprint arXiv:1807.05444 , 2018

    2018 arXiv

  28. [36]

    A data-driven approach to incident management: Enhancing devops operations with machine learning-based root cause analysis

    Venkata Mohit Tamanampudi. A data-driven approach to incident management: Enhancing devops operations with machine learning-based root cause analysis. Distributed Learning and Broad Applications in Scientific Research , 6:419--466, 2020

    2020

  29. [37]

    Data-driven decisions: Integrating machine learning into human resource and financial management

    S Venkatesan, TV Ambuli, Kabirdoss Devi, K Sampath, and S Kumaran. Data-driven decisions: Integrating machine learning into human resource and financial management. In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT) , volume 1, pages 1829...

    2024

  30. [38]

    Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches

    Ahsan Waqar. Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches. Expert Systems with Applications , 249:123503, 2024

    2024

  31. [39]

    Behavior regularized offline reinforcement learning

    Yifan Wu, George Tucker, and Ofir Nachum. Behavior regularized offline reinforcement learning. arXiv preprint arXiv:1911.11361 , 2019

    1911 arXiv

  32. [40]

    A theoretical analysis of ndcg ranking measures

    Wang Yining, Wang Liwei, Li Yuanzhi, He Di, Chen Wei, and Liu Tie-Yan. A theoretical analysis of ndcg ranking measures. In Proceedings of the 26th annual conference on learning theory , 2013

    2013

  33. [41]

    Partial and cost-minimized computation offloading in hybrid edge and cloud systems

    Haitao Yuan, Jing Bi, Ziqi Wang, Jinhong Yang, and Jia Zhang. Partial and cost-minimized computation offloading in hybrid edge and cloud systems. Expert Systems with Applications , 250:123896, 2024

    2024

  34. [42]

    Icassp-spgc 2022: Root cause analysis for wireless network fault localization

    [dataset] Tianjian Zhang, Qian Chen, Yi Jiang, Dandan Miao, Feng Yin, Tao Quan, Qingjiang Shi, and Zhi-Quan Luo. Icassp-spgc 2022: Root cause analysis for wireless network fault localization. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Pro...

    2022

  35. [43]

    Lemma-rca: A large multi-modal multi-domain dataset for root cause analysis

    [dataset] Lecheng Zheng, Zhengzhang Chen, Dongjie Wang, Chengyuan Deng, Reon Matsuoka, and Haifeng Chen. Lemma-rca: A large multi-modal multi-domain dataset for root cause analysis. arXiv preprint arXiv:2406.05375 , 2024

    2024 arXiv

  36. [44]

    A survey of machine learning for urban decision making: Applications in planning, transportation, and healthcare

    Yu Zheng, Qianyue Hao, Jingwei Wang, Changzheng Gao, Jinwei Chen, Depeng Jin, and Yong Li. A survey of machine learning for urban decision making: Applications in planning, transportation, and healthcare. ACM Computing Surveys , 2024

    2024

  37. [45]

    p-causality: Identifying spatiotemporal causal pathways for air pollutants with urban big data

    [dataset] Julie Yixuan Zhu, Chao Zhang, Huichu Zhang, Shi Zhi, Victor OK Li, Jiawei Han, and Yu Zheng. p-causality: Identifying spatiotemporal causal pathways for air pollutants with urban big data. arXiv preprint arXiv:1610.07045 , 2018

    arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.